most citedBeyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats

2 citations · 2 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CL2026

TRACER: Early Failure Detection for Task-Oriented Dialogue

Erfan Nourbakhsh, Rocky Slavin, Ke Yang +1

Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present…

cs.CL2026

When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG

Erfan Nourbakhsh, Rocky Slavin, Ke Yang +1

Medical question answering is a high-stakes setting where factual errors can have serious consequences. Retrieval-augmented generation (RAG) is widely viewed as a promising solutio…

cs.LG2026

MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data

Amir Mousavi, Mohammad Sadegh Sirjani, Erfan Nourbakhsh +5

Real-time cognitive load assessment from eye-tracking signals could enable adaptive human-centered AI in safety-critical applications such as driver vigilance monitoring or automat…

cs.LG2026

CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment

Amir Mousavi, Erfan Nourbakhsh, Mohammad Sadegh Sirjani +5

Assessing cognitive load continuously and at low latency would help adaptive human-computer interaction, but it remains hard because labeled data are scarce and models generalize p…

cs.CL2026

Prompting Underestimates LLM Capability for Time Series Classification

Dan Schumacher, Erfan Nourbakhsh, Rocky Slavin +1

Prompt-based evaluations suggest that large language models (LLMs) perform poorly on time series classification, raising doubts about whether they encode meaningful temporal struct…

cs.SE2024

An Analysis of Automated Use Case Component Extraction from Scenarios using ChatGPT

Pragyan KC, Rocky Slavin, Sepideh Ghanavati +2

Mobile applications (apps) are often developed by only a small number of developers with limited resources, especially in the early years of the app's development. In this setting,…